Search Results (2,353)

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

As automated vehicles move from controlled environments to unpredictable real-world roads, scenario-based testing has become the cornerstone of safety validation. In recent years, substantial progress has been made in scenario representation standards and generation methodologies. However, integrating scenario generation, standards-aligned packaging, validation, curation, and structured querying into a reproducible end-to-end lifecycle remains challenging in practice. This work presents a reproducible reference architecture for Scenario Databases (SCDBs) that treats scenario collections as lifecycle-governed data systems rather than static repositories. The proposed architecture unifies the scenario lifecycle within a single workflow. It integrates scenario generation and ingestion, validation and curation, immutable storage, semantic and value-based querying, and reproducible export. Scenario semantics are represented using ASAM OpenX formats (OpenDRIVE and OpenSCENARIO), together with ASAM OpenLABEL metadata, enabling standards-aligned interoperability. Querying is performed over categorical and value-carrying metadata without requiring inspection of raw scenario artifacts at query time. The reference implementation is deployed using Infrastructure-as-Code, supporting reproducibility and low operational overhead. Execution-based metric enrichment is supported as an optional extension, enabling scenarios to be augmented with execution-derived measurements and trace metadata. The contribution is not a centralized database, but a reference architecture and deployment blueprint that supports interoperable and federated scenario ecosystems. By framing SCDBs as reproducible lifecycle systems, this work supports scalable scenario reuse and more transparent safety validation workflows. Full article

Sensory Architecture has been recognized as a relevant factor in the emotional experience of children and adolescents with autism spectrum disorder (ASD); however, a persistent gap remains in the systematic incorporation of empirical evidence into the architectural design process, particularly in Latin American urban contexts. Within this framework, the present study analyzed the relationship between Sensory Architecture and Emotional Well-Being in children and adolescents with ASD attending therapeutic centers in the district of San Juan de Lurigancho, Lima, with the aim of translating empirical findings into evidence-based architectural design criteria. A quantitative, non-experimental, cross-sectional, and correlational approach was adopted. The unit of analysis consisted of children and adolescents with ASD, whose emotional experience was assessed through proxy informants, specifically family members. The sample comprised 100 family informants selected using non-probabilistic convenience sampling. Data were collected through a structured questionnaire consisting of 25 items measured on a five-point Likert scale, which demonstrated high internal consistency (Cronbach’s alpha = 0.93). As the data did not follow a normal distribution (Kolmogorov–Smirnov, p < 0.05), Spearman’s Rho coefficient was applied. The results revealed positive and statistically significant associations between the dimensions of Sensory Architecture and Emotional Well-Being, with Spatial Configuration emerging as the dimension with the strongest associative weight ($\rho$ = 0.652; p < 0.001). Based on this empirical hierarchy, an evidence-based architectural design proposal for a therapeutic center was developed. Study limitations include the cross-sectional design and the absence of post-occupancy evaluation, which point to future research directions focused on longitudinal studies and empirical validation of architectural performance. Full article

We developed a real-time alert and data management framework that integrates programmable logic controllers, RS-485 industrial communication, Structured Query Language Server, Message Queuing Telemetry Transport (MQTT), and the nodemation (n8n) automation platform, using a filling machine production line as a case study. The system collects and analyzes the operational status and production line data of the filling machine in real time, storing all information in a database for preservation. Through MQTT, the data is sent to n8n for automated processing. When equipment anomalies occur or data exceed predefined thresholds, the system automatically notifies maintenance personnel via communication software APIs. Additionally, users can query daily production capacity or related data using n8n’s AI functions. This architecture offers low cost, rapid deployment, cross-platform integration, and high flexibility. It not only improves anomaly handling efficiency but also preserves complete historical records, supporting trend analysis, report generation, and decision optimization, thereby assisting the filling production line in achieving long-term stable and intelligent management. Full article

Aiming to address the problem of extracting the remote sensing FTIR spectral characteristics of the hot jet of a certain type of aero-engine under different working conditions, this paper proposes a feature construction algorithm for the remote sensing FTIR spectral characteristics of the aero-engine hot jet based on the fusion of the original spectral features and the deep spectral features. The infrared spectrum was collected at a distance of 280 m, covering the spectral range of 2.5–15 μm with a resolution of 1 cm⁻¹. The Neighborhood–Autoencoder Integration Dual-Branch Network (NAIDN) feature construction algorithm is proposed. This algorithm contains a neighborhood integration branch and an autoencoder branch. The neighborhood integration branch converts the radiation intensity values of discrete wavenumber points into local energy aggregation features through a sliding window, accurately extracting the key physical information in the original spectrum. The autoencoder branch uses a three-layer fully connected neural network architecture to mine the deep spectral features of the spectral data. The algorithms of the two branches not only retain the physical interpretability of spectral analysis but also capture the multi-parameter coupling information hidden in the hot jet spectrum through the representation learning ability of the autoencoder, achieving feature fusion across spatial dimensions. Compared with traditional feature construction algorithms, the dual-branch feature construction algorithm proposed in this paper has stronger comprehensive representation capabilities. The content of carbon dioxide (CO₂) and cyanide groups (-C≡N) in the hot jet under different operating conditions varies significantly. In the experiment, an unsupervised clustering algorithm, the Agglomerative Clustering classifier, is selected, and the classification accuracy of the features extracted by the algorithm in this paper reaches 92.97% on this classifier, thereby verifying the effectiveness of the algorithm in this paper. Full article

In recent decades, the Ucayali region, the main territory of the Asheninka communities, has experienced increasing socio-environmental pressures associated with climate change, educational inequality, and territorial vulnerability in rural and indigenous contexts. In response, this research proposes the design of a sustainable agricultural school for the Asheninka community, conceived as an educational building that integrates biophilic strategies to enhance environmental performance and spatial quality. The methodological approach comprises a literature review, site-specific environmental analysis based on hydrometeorological data, and the development of an architectural proposal focused on sustainable building design. Digital tools such as Revit and SketchUp were employed alongside official climatic data sources to support design decision-making. The proposal includes twelve biophilic agricultural classrooms incorporating passive design strategies, rainwater harvesting systems with a capacity of 22.5 m³ per day per classroom, and photovoltaic-powered public lighting systems. Results indicate that the integration of natural ventilation, green infrastructure, and locally sourced materials contributes to significant improvements in thermal comfort, humidity control, and energy autonomy within the educational facilities. The architectural complex is complemented by green corridors and collective open spaces that reinforce environmental performance at the site scale. This study demonstrates that sustainable educational buildings adapted to local ecosystems and climatic conditions can function as effective infrastructures for environmental mitigation and resilient rural development, contributing to more sustainable forms of urban and rural living. Full article

Architectural heritage plays a central role in shaping visitors’ emotional experiences within cultural tourism contexts. However, empirical research examining how specific architectural styles evoke emotional responses remains limited, particularly when using objective measurement techniques. This study investigates emotional reactions to architectural heritage by applying the Stimulus–Organism–Response (SOR) theoretical framework. In this model, architectural styles act as environmental stimuli, emotional processing represents the organismic state, and the resulting emotional activation constitutes the response. An experimental protocol was conducted with a sample of 645 participants exposed to a series of standardized architectural heritage images representing different architectural styles and infrastructure types. Emotional reactions were captured in real time through facial emotion recognition technology, enabling the objective measurement of eight basic emotions: neutral, happiness, sadness, surprise, fear, disgust, anger, and contempt. The collected emotional data were statistically analyzed using Analysis of Variance (ANOVA) to identify significant differences in emotional responses across architectural styles, heritage typologies, and gender. When significant differences were detected, Tukey’s HSD post hoc tests were applied to determine specific group contrasts. The findings reveal that different architectural styles generate distinct emotional patterns, highlighting the role of architectural aesthetics as a powerful mediator of affective engagement with heritage environments. From a theoretical perspective, this research contributes to heritage tourism and environmental psychology by integrating the SOR framework with real-time emotion detection technologies, providing a novel methodological approach for analyzing emotional responses to architectural heritage. Full article

This study demonstrates a hierarchical spectral modelling approach for predicting pasture nutrition metrics using TabPFN (Tabular Prior-Data Fitted Network), a transformer-based machine learning architecture. In the face of climate variability, aligning stocking rates with pasture resources is crucial for sustainable livestock grazing, requiring accurate assessments of both pasture biomass and nutrient composition. Our research, conducted across diverse growth stages at five tropical and subtropical savanna rangeland properties in Queensland, Australia, with native and introduced C4 grasses, employed a hierarchical sampling and modelling strategy that scales from laboratory spectroscopy to Sentinel-2 satellite predictions via uncrewed aerial vehicle (UAV) hyperspectral imaging. Spectral data were collected from leaf (laboratory spectroscopy) through field (point measurements), UAV hyperspectral imaging, and Sentinel-2 satellite imagery. Traditional laboratory wet chemistry methods determined plant leaf and stem nutrient content, from which crude protein (CP = total nitrogen (TN) × 6.25) and dry matter digestibility (DMD = 88.9–0.779 × acid detergent fibre (ADF)) were derived. TabPFN models were trained at each spatial scale, achieving validation ${\mathrm{R}}^2$ of 0.76 for crude protein at the leaf scale, 0.95 at the UAV scale, and 0.92 at the Sentinel-2 satellite scale. For dry matter digestibility, validation ${\mathrm{R}}^2$ was 0.88 at the UAV scale and 0.73 at the Sentinel-2 scale. A pasture classification masking approach using a deep neural network with 98.6% accuracy (7 classes) was implemented to focus predictions on productive pasture areas, excluding bare soil and woody vegetation. The Sentinel-2 models were trained on 462 samples from 19 site–date combinations across 11 field sites. The TabPFN architecture provided notable advantages over traditional neural networks: no hyperparameter tuning required, faster training, and superior generalisation from limited training samples. These results demonstrate the potential for accurate and efficient prediction and mapping of pasture quality across large areas (100 s–1000 s ${\mathrm{k}\mathrm{m}}^2$) using freely available satellite imagery and open-source machine learning frameworks. Full article

For Unmanned Aerial Vehicles (UAVs), optical sensing for reliable landing and the detection of the landing area is a crucial element. In low-light conditions, at night, and in foggy weather, where optical sensing is not feasible, thermal imaging can be utilised. Although this situation has been widely researched, most UAV landing approaches rely on GNSS assistance or single-mode detection, which limits their robustness and scalability in real-world operations. This study proposes an actively heated thermal helicopter landing pad designed using electrically powered resistive heating elements and a high-emissivity surface coating. Furthermore, optical and thermal images collected during actual UAV flight experiments under daytime and night-time conditions were processed using image fusion techniques with AVGF, DWTF, GPF, LPF, MPF, and HWTF fusions, and their performance in deep learning models was compared. The obtained optical, thermal, and fused datasets are used to train and evaluate deep learning-based helicopter landing pad detection models based on the YOLOv8 architecture. Experimental results show that models trained with single-mode data exhibit limited cross-domain generalisation, while fusion-based learning significantly improves detection robustness in optical and thermal domains. Among the evaluated methods, LPF, MPF and HWTF provide the most consistent performance improvements. The findings indicate that electrically heated thermal helicopter landing pads, when combined with image fusion and deep learning-based detection, can increase the landing detectability of UAVs at night and in low-visibility conditions. This detection-focused approach contributes to UAV flight safety by enhancing the visibility of the landing area without relying on active infrared markers or additional navigation infrastructure. Full article

Background: A major technical challenge in single-cell transcriptomics is the absence of an integrative analytic pipeline that can simultaneously leverage gene regulatory network (GRN) architecture, AI-assisted gene panel discovery, and functional relevance analyses to generate coherent biological insights. Existing approaches often treat these components independently, focusing on clusters, marker genes, or predictive features without integrating them into a mechanistically grounded framework. Consequently, comprehensive screening that links regulatory association, gene signature screening, and functional interpretation within single-cell datasets remains limited, underscoring the need for an integrated strategy. Methods: We developed an integrative bioinformatics pipeline based on Gene regulatory network–AI–Functional Analysis (GAFA), combining latent-space integration, unsupervised clustering, diffusion pseudotime analysis, lineage-resolved generalized additive modeling, GRN inference, and machine learning-based gene panel discovery. This framework enables systematic mapping of cell-state structure, reconstruction of differentiation and effector trajectories, and identification of transcriptional and regulatory features strongly associated with clinical outcomes. As a case study, we applied the pipeline to NK cell transcriptomes from six CML patients (two early relapse, two late relapse, two durable treatment-free remission—TFR; 15 samples) collected at TKI discontinuation and 6–12 months after therapy cessation. Results: We reanalyzed publicly available scRNA-seq data from a previously published CML cohort to evaluate NK-cell transcriptional programs associated with treatment-free remission and relapse. We resolved six transcriptionally distinct NK cell states spanning CD56^bright-like cytokine-responsive, early activated, terminally mature, cytotoxic, lymphoid trafficking, and HLA-DR⁺ immunoregulatory populations, each exhibiting outcome-specific compositional differences. Pseudotime analysis revealed two major NK cell lineages—a maturation trajectory and a cytotoxic effector trajectory. TFR samples displayed balanced occupancy of both lineages, whereas early relapse samples showed marked depletion of the maturation branch and preferential accumulation in cytotoxic end states. AI-guided feature selection and random forest modeling identified an 18-gene panel that distinguished NK cells from TFR and relapse samples in an exploratory manner. Among them, CST7, FCER1G, GNLY, GZMA, and HLA-C were conventional NK-associated genes, whereas ACTB, CYBA, IFITM2, IFITM3, LYZ, MALAT1, MT2A, MYOM2, NFKBIA, PIM1, S100A8, S100B, and TSC22D3 were novel. The GRN inference further uncovered outcome-specific regulatory modules, with RUNX3, EOMES, ELK4, and REL regulons enriched in TFR, whereas FOSL2 and MAF regulons were enriched in relapse, and their downstream targets linked to IFN-γ signaling, metabolic reprogramming, and immunoregulatory feedback circuits. Conclusions: This AI-enabled single-cell analysis demonstrates how NK cell state composition, differentiation trajectories, and regulatory network rewiring collectively shape TFR versus relapse following TKI discontinuation in CML. The integrative pipeline provides a modular framework that could be extended to additional datasets for data-driven biomarker discovery and mechanistic stratification, and highlights candidate transcriptional regulators and NK cell programs that may be leveraged to improve remission durability, pending validation in larger patient cohorts. Full article

Background/Objectives: This study aimed to develop and evaluate deep learning models for the detection, localization, and classification of impacted maxillary canines, and to compare their performance with cone-beam computed tomography (CBCT) as the reference standard. Methods: This cross-sectional diagnostic accuracy study was conducted at King Abdulaziz University Dental Hospital to develop and validate artificial intelligence (AI) models for detecting and localizing maxillary canine impactions using panoramic and cone-beam computed tomography (CBCT) imaging data. A total of 641 panoramic ra and 158 CBCT scans were collected, of which 158 cases had matched panoramic–CBCT pairs for localization analysis. Images were annotated and validated by expert radiologists and orthodontists, with consensus review ensuring labeling reliability. Data augmentation expanded each panoramic and CBCT category to 500 samples for panoramic and 1000 samples for CBCT, resulting in 1935 panoramic and 5703 CBCT images after preprocessing and normalization. The datasets were divided into (training + validation) (80%) and testing (20%) subsets. MobileNetV2 architectures were used for classification, and whdiographsile, a ResNet-50–based Few-Shot Learning framework, enabled spatial localization of impacted canines. Models were trained using the Adam optimizer with a learning rate of 1 × 10⁻⁴ and evaluated using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Cohen’s kappa and 95% confidence intervals were used to assess agreement between AI predictions and expert annotations. Results: Panoramic classification achieved 94% accuracy, demonstrating the highest performance in normal cases and reduced recall for bilateral impactions. The CBCT classifier achieved 98% accuracy across positional categories. Cross-modality prediction reached 93.5% accuracy, with strong agreement compared to CBCT (Cohen’s kappa = 0.91). Expert review confirmed reliable localization of impacted canines on both imaging modalities. Conclusions: Artificial intelligence applied to panoramic radiographs supports the detection, localization, and characterization of impacted maxillary canines with performance comparable to CBCT. This approach may enable lower-radiation decision support for clinical triage. Full article

This study focuses on an integrated conceptual framework for smart municipal waste management that combines Pay-as-you-throw (PAYT) with digital technologies emerging from the Internet of Things (IoT), Artificial Intelligence, and blockchain. In the literature, a key limitation has long been recognised: the fragmented implementation of technological solutions and economic instruments in waste management systems. This model is proposed as a multi-layer architecture that integrates user identification, real-time data collection, predictive optimisation, and automated tariff calculation. The framework is expected to reduce mixed-waste volumes and improve operational efficiency while ensuring traceability and transparency in waste management. The framework also provides a practical basis for implementing circular economy principles and advancing climate and urban sustainability goals by linking user behaviour to cost allocation and data-driven monitoring. The findings highlight that measurable environmental benefits depend on the structural integration of behavioural incentives, real-time monitoring, and transparent data governance. The framework demonstrates how PAYT-based incentives, combined with digital monitoring, can reduce mixed waste volumes and associated emissions. Full article

This study presents a novel neural parameter diffusion approach (FWA-PDiff) designed to enhance data quality. To address the limitations of conventional diffusion models—such as inefficient sampling and insufficient feature sensitivity, which may compromise output fidelity—this study introduces four key innovations. First, the proposed model introduces an adaptive recalibration of the sampling frequency in the Fourier domain to optimize feature extraction for image data. Second, a dual-channel autoencoder architecture is employed, featuring a multi-scale, fine-grained encoder (MFE) that enables the simultaneous capture of features at multiple resolutions. Third, a wavelet-attention mechanism (WA) is incorporated into the decoder to highlight subtle high-frequency details. Fourth, the proposed model introduces a hybrid loss function that combines Mean Squared Error (MSE) and Kullback–Leibler (KL) divergence to improve data reconstruction. Collectively, these improvements enable the generation of high-fidelity parameters, thereby contributing to enhanced data quality. Extensive experiments conducted on benchmark datasets—including MNIST, CIFAR-10, CIFAR-100, and STL-10—demonstrate the effectiveness of the proposed approach, which consistently achieves superior performance in improving data quality. Full article

Growing amounts of municipal waste and the need for efficient recycling demand automated and accurate classification systems. This paper investigates deep learning approaches for multi-class waste sorting based on image data, comparing three widely used convolutional neural network architectures (ResNet-50, EfficientNet-B0, and MobileNet V3) with a custom hybrid model (CustomNet). The dataset comprises 13,933 RGB images across 10 waste categories, combining publicly available samples from the Kaggle Garbage Classification dataset (61.1%) with images collected in house (38.9%). The three glass sub-categories (brown, green, and white glass) were merged into a single glass class to ensure consistent class representation across all dataset splits. Preprocessing steps include normalization, resizing, and extensive data augmentation to improve robustness and mitigate class imbalance. Transfer learning is applied to pretrained models, while CustomNet integrates feature representations from multiple backbones using projection layers and attention mechanisms. Performance is evaluated using accuracy, macro-F1, and ROC–AUC on a held-out test set. Statistical significance was assessed using paired t-tests and Wilcoxon signed-rank tests with Bonferroni correction across five-fold cross-validation runs. The results show that CustomNet achieves 97.79% accuracy, a macro-F1 score of 0.973, and a ROC–AUC of 0.992. CustomNet significantly outperforms EfficientNet-B0 and MobileNet V3 ($p<0.001$, Bonferroni corrected), and it achieves performance parity with ResNet-50 ($p=0.383$) at a substantially lower parameter count in the classification head (9.7 M vs. 25.6 M). These findings indicate that combining multiple feature extractors with attention mechanisms improves classification performance, supports qualitative model explainability via saliency visualization (Grad-CAM), and enables practical deployment on heterogeneous Edge AI platforms. Inference benchmarking on an NVIDIA Jetson Orin Nano demonstrated real-world deployment feasibility at 86.70 ms per image (11.5 FPS). Full article

Growth and egg production are the two most economically important traits in goose production systems. However, negative genetic correlations between these traits make it difficult to achieve balanced genetic improvement through selection. In this study, we analyzed whole-genome resequencing data from 1033 Zhedong White Geese to identify genetic variants related to birth weight (BW), body weight at 90 days (BW90), and egg number at 66 weeks of age (EN66). Single-trait genome-wide association studies (GWASs) identified 6, 5, and 5 lead SNPs significantly associated with BW, BW90, and EN66, respectively. By integrating network analysis, PLACO, and multivariate linear mixed models (mvLMMs), we further identified KCNAB2 and KCND3 as potential pleiotropic candidate genes influencing both growth and egg production. Notably, the variant at CHR25: 6006715, located within an intronic region of KCND3, was associated with increased BW (ZscoreBW = 4.44) and decreased EN66 (ZscoreEN66 = −3.55), showing strong pleiotropic significance (P_PLACO = 4.88 × 10⁻⁸). Collectively, these findings provide new insights into the genetic architecture underlying the antagonistic relationship between growth and egg production in geese and offer valuable genetic targets for developing breeding strategies that jointly optimize growth performance and reproductive efficiency. Full article